A multicenter study of asymmetric and symmetric dimethylarginine as predictors of mortality risk in hospitalized COVID-19 patients

Mortality of patients hospitalized with COVID-19 has remained high during the consecutive SARS-CoV-2 pandemic waves. Early discrimination of patients at high mortality risk is crucial for optimal patient care. Symmetric (SDMA) and asymmetric dimethylarginine (ADMA) have been proposed as possible biomarkers to improve risk prediction of COVID-19 patients. We measured SDMA, ADMA, and other L-arginine-related metabolites in 180 patients admitted with COVID-19 in four German university hospitals as compared to 127 healthy controls. Patients were treated according to accepted clinical guidelines and followed-up until death or hospital discharge. Classical inflammatory markers (leukocytes, CRP, PCT), renal function (eGFR), and clinical scores (SOFA) were taken from hospital records. In a small subgroup of 23 COVID-19 patients, sequential blood samples were available and analyzed for biomarker trends over time until 14 days after admission. Patients had significantly elevated SDMA, ADMA, and L-ornithine and lower L-citrulline concentrations than controls. Within COVID-19 patients, SDMA and ADMA were significantly higher in non-survivors (n = 41, 22.8%) than in survivors. In ROC analysis, the optimal cut-off to discriminate non-survivors from survivors was 0.579 µmol/L for SDMA and 0.599 µmol/L for ADMA (both p < 0.001). High SDMA and ADMA were associated with odds ratios for death of 11.45 (3.37–38.87) and 5.95 (2.63–13.45), respectively. Analysis of SDMA and ADMA allowed discrimination of a high-risk (mortality, 43.7%), medium-risk (15.1%), and low-risk group (3.6%); risk prediction was significantly improved over classical laboratory markers. We conclude that analysis of ADMA and SDMA after hospital admission significantly improves risk prediction in COVID-19.


Study participants
We identified 394 patients with COVID-19 who were included in one of the four participating biobanks.After exclusion of patients with missing serum samples (N = 59), patients with missing outcome data (N = 155), and patients for whom no serum sample was available from days 1-4 after hospital admission (N = 53), we included 180 patients who were admitted with symptomatic COVID-19 to one of the four participating medical centers between January 02, 2020 and January 22, 2021 (Fig. 1).Patients were included if the main cause for hospitalization was COVID-19; all patients had a positive SARS-CoV-2 PCR test result in respiratory samples analyzed on admission or externally prior to admission.Participating centers were the University Medical Center Hamburg-Eppendorf (UKE), the University Clinic Aachen (UKA), the Medical School of Hannover (MHH), and the Charité Berlin (ChB).

Assessment of clinical patient status
Patients were assessed as eligible based on a positive RT-PCR test for SARS-CoV-2 in a respiratory tract sample as previously described 22 .Vital parameters presented in this study were taken between four and 24 h following hospital admission or intubation.The SOFA score was recorded as a measure of clinical patient status 23 .Acute respiratory distress syndrome (ARDS) was diagnosed according to the Berlin definition 24 .Acute kidney injury was defined according to the AKIN criteria 25 and/or need for continuous veno-venous hemofiltration in patients with no pre-existing chronic renal failure.Patients with a body mass index (BMI) of 25 to < 30 kg/m 2 were classified as overweight and those with BMI ≥ 30 kg/m 2 as obese.Diabetes and prediabetes were defined by clinical history, medication and HbA1c values ≥ 6.5% or ≥ 5.7 to < 6.5%, respectively.Serum and whole blood samples were obtained routinely at the time of admission.Complete blood count, coagulation tests, inflammatory markers [circulating levels of C-reactive protein (CRP), pro-calcitonin (PCT)] and creatinine levels in blood were measured among other tests.Creatinine clearance was estimated using the CKD-EPI formula 26 .

Statistical analyses
All variables were tested for normal distribution using the Kolmogorov-Smirnov test.Data are presented as mean with standard deviation (SD).Differences between groups were tested for significance using the nonparametric Mann-Whitney U test for two groups or the Kruskal-Wallis analysis of variance for more than two groups.The Chi 2 test was used for comparison of categorical variables between groups.Time courses of ADMA and SDMA concentrations were examined using repeated measures two-way ANOVA followed by Tukey's multiple comparisons test.Spearman's rank correlation was used to assess pairwise correlations.Survival analyses were performed using Kaplan-Meier curves comparing patients with ADMA and SDMA above or below the cut-off value determined in receiver-operated curve (ROC) analyses.The Youden index was calculated to identify the optimal cut-off for biomarkers 27 .Hazard ratios (HR) and 95% confidence intervals (CI) were calculated by multivariable-adjusted logistic regression analyses.As we had identified two biomarkers, ADMA and SDMA, as predictors of COVID-19 mortality, we analyzed additional models using (SDMA + ADMA) or (SDMA × ADMA) as variables, respectively.In addition, we performed a decision tree analysis to determine risk upon sequential analysis of SDMA and ADMA.Cut-offs to separate risk groups were based on values determined in ROC analysis for both biomarkers.All statistical analyses were performed using SPSS (version 25; IBM Corporation, Armonk, NY, USA) and GraphPad Prism (version 6.01, GraphPad Software, San Diego, CA, USA).For all tests, p < 0.05 was considered statistically significant.

Baseline characteristics
Patients had a mean age of 62.1 ± 15.4 years (range 22-91 years); 64/180 patients (35.6%) were female.Hospital admission was on day 6.7 ± 6.2 after the onset of COVID-19 symptoms.Baseline patient characteristics are shown in Table 1 for the entire cohort and for patients who survived or died during hospitalization.The first blood sample was taken on days 1-4 after hospital admission (mean, day 2.0 ± 1.2).Mean age of controls was 54.9 ± 11.9 years (range 22-74 years) and 44/127 (34.6%) of controls were women.According to their status as blood donors, none of the control subjects had major chronic diseases, nor fever or any acute signs of infection; their routine laboratory analyses showed no pathological findings.We were unable to include healthy controls for COVID-19 patients older than 74 years, as individuals in the age range above that age were not present amongst blood donors.Thus, matching of COVID-19 patients and health controls for age was incomplete.

Clinical course of the patients
The mean duration of hospitalization was 19.7 ± 20.6 days.41 patients (22.8%) died during hospitalization.81 patients (45.0%) were classified as having acute respiratory distress syndrome (ARDS), and were treated in ICU.51 patients (28.3%) received mechanical ventilation, and 35 patients (19.4%) were treated with ECMO.The rates of ARDS and subsequent ICU treatment, mechanical ventilation, and ECMO therapy were significantly higher in the subgroup of patients who died than in survivors (Table 1).Survivors had lower SOFA scores at admission; also, inflammatory laboratory markers like leukocyte count, CRP, and pro-calcitonin taken at the same time as L-arginine-related biomarkers were significantly higher in patients who died than in survivors (Table 1).

Concentrations of L-arginine-associated biomarkers in COVID-19 patients versus controls
The concentrations of ADMA and SDMA in the first available serum sample after hospital admission were significantly higher in COVID-19 patients than in healthy blood donors (Fig. 2a,b).The serum concentration www.nature.com/scientificreports/ of L-ornithine was significantly higher and that of L-citrulline was significantly lower in COVID-19 patients than in controls, whilst there was no significant difference in L-arginine concentration (Table 2, supplementary Figure 1).These differences in biomarker concentrations resulted in a significant elevation of the L-ornithine/Larginine ratio (Orn/Arg Ratio), whilst the L-citrulline/L-arginine ratio (Cit/Arg Ratio) was significantly reduced in COVID-19 patients versus controls (Fig. 2c,d).When we restricted our analysis to fully matched COVID-19 patients and controls, the differences in mean metabolite concentrations remained unchanged (Supplementary Table 1).Within the group of COVID-19 patients, ADMA and SDMA concentrations were significantly higher in patients who died during hospitalization than in survivors (Fig. 2a,b), as was L-ornithine concentration, whereas  www.nature.com/scientificreports/there were no significant differences in L-arginine and L-citrulline concentrations (Table 2).The Orn/Arg Ratio and the Cit/Arg Ratio showed no significant differences between survivors and non-survivors (Fig. 2c,d).

Associations of ADMA and SDMA with mortality in COVID-19
In Both biomarkers showed no significant associations in models adjusted for age, sex, and eGFR or PCT (Table 3).

Associations of combined ADMA and SDMA with mortality in COVID-19
Next, we tested whether the combined analysis of SDMA and ADMA improved predictive power using a decision tree algorithm.Sequential measurements of SDMA and ADMA significantly enhanced the discrimination of mortality risk (Fig. 4).Patients with both, high SDMA and high ADMA concentrations had a high mortality risk of 43.7%, as compared to an intermediate risk of 15.1% in patients with either SDMA or ADMA elevated, and a low mortality risk of 3.6% in patients with both ADMA and SDMA low (Fig. 5).The odds ratio for patients in the  intermediate risk group versus low risk group was 4.80 (1.03-23.76;p = 0.049); the odds ratio in the high versus low risk group was 20.93 (4.73-92.62;p < 0.0001).In ROC analyses, (ADMA + SDMA) and (ADMA x SDMA) resulted in AUCs of 0.734 (0.648-0.821) and 0.741 (0.654-0.828), respectively (both p < 0.0001) (Supplementary Figure 2).

Improved prediction of disease severity with ADMA and SDMA compared to classical inflammatory laboratory markers
The well-established inflammatory laboratory markers, leukocyte cell count, C-reactive protein (CRP), and pro-calcitonin (PCT), as well as the SOFA score at admission were all significantly associated with mortality risk, with the relative risks for total mortality associated with each of these traditional markers lying in the range of 2-threefold (Table 4).Amongst combinations of these classical risk markers, SOFA and leukocyte count, SOFA and PCT, CRP and leukocyte count, and CRP and PCT produced the strongest increases in hazard ratio (Supplementary Figure 3a).
We next tested the combination of traditional laboratory markers and SOFA with SDMA and ADMA (Table 5).SOFA score in combination with ADMA, SDMA, or their combination did not result in improved risk prediction (Supplementary Figure 3b), nor did leukocyte count or PCT (Supplementary Figure 3c,d).By Table 4. ROC analyses for ADMA, SDMA, and traditional laboratory markers versus mortality.*The optimal cut-off for discrimination of survivors and non-survivors was determined for each variable by applying the Youden procedure 27 .# Hazard Ratios were calculated for comparison mortality risk of patients with risk marker levels above as compared to equal or below the cut-off value.ADMA, asymmetric dimethylargin-ine; CRP, C-reactive protein; PCT, pro-calcitonin; SDMA, symmetric dimethylarginine; SOFA, sequential organ failure assessment.www.nature.com/scientificreports/contrast, the combination of CRP with ADMA alone, (ADMA + SDMA), and (ADMA x SDMA) resulted in the greatest increases in hazard ratio (Supplementary Figure 3e).COVID-19 patients with elevated CRP and elevated (ADMA x SDMA) had a hazard ratio for mortality of 10.0 (1.56-64.23),p < 0.0001.

Time course of ADMA and SDMA during clinical treatment in COVID-19 survivors and non-survivors
Consecutive blood samples from 23 COVID-19 patients (12 survivors, 11 non-survivors) were available for analysis.The serum concentrations of SDMA and ADMA significantly increased in non-survivors during the first 14 days in hospital, i.e., in the period before their death.By contrast, both biomarkers showed stable concentrations over time in survivors (Fig. 6a-d).As a consequence, the L-arginine/ADMA ratio significantly decreased over time in non-survivors (Supplementary Figure 4a).The time course of the L-citrulline/L-arginine ratio showed no clear differentiation between survivors and non-survivors (Supplementary Figure 4b), whereas we observed a significant increase in L-ornithine/L-arginine ratio over time in non-survivors (Supplementary Figure 4c).

Discussion
Our study provides evidence from a multicenter study that quantification of ADMA and SDMA in blood samples taken as early as possible after admission of COVID-19 patients to hospital contribute significantly to enhanced estimation of patients' mortality risk during in-hospital treatment.Whilst each of the two biomarkers alone had limited prognostic value and was not superior to classical risk determinants in routine clinical chemistry and clinical scoring, addition of either ADMA or SDMA to such traditional risk predictors significantly improved the predictive power.Sequential analysis of both, ADMA and SDMA, allowed discrimination of a high risk, medium risk, and low risk group of COVID-19 patients with greatly different rates of in-hospital mortality.In addition, the time courses of ADMA and SDMA in repetitive blood samples during the first two weeks of hospitalization showed significant dichotomy between survivors and non-survivors, respectively.The hospital mortality rate in the patient population included in this study was 20.7%, which compares to 29% in our pilot study 15 , 11% in the study by Sozio and co-workers 19 , and 22% in another study by Karacaer and co-workers 18 .Non-survivors in our study were significantly older than survivors, had lower systolic and diastolic blood pressure and higher systemic inflammatory markers, and required more intensive treatment including extracorporeal membrane oxygenation in a high percentage of patients.These differences in clinical characteristics were mirrored by differences in L-arginine-related biomarkers: As compared to controls, COVID-19 patients had higher L-ornithine concentrations, indicative of arginase activation, as well as higher L-citrulline concentrations, indicative of increased NOS enzymatic activity, whereas L-arginine concentrations were not significantly different.L-ornithine concentrations were also significantly elevated in non-survivors as compared to survivors, and the Orn/Arg Ratio significantly increased over time in non-survivors as opposed to survivors.
Most notably, however, the dimethylated L-arginine metabolites, ADMA and SDMA, were elevated in COVID-19 patients compared to controls; both were higher in non-survivors than in survivors, and they showed a further increase in non-survivors during the first two weeks of hospitalization.Taken together, these two biomarkers made it possible to significantly discriminate between COVID-19 patients with low, intermediate, and high mortality risk.Further, these two biomarkers significantly enhanced the predictive power of classical inflammatory markers.Thus, our study proves that ADMA and SDMA serum concentrations are suitable risk markers in severely diseased COVID-19 patients, and they corroborate our previous findings as well as those reported for small patient samples by other investigators.Using an untargeted metabolomics profiling approach in 27 COVID-19 patients compared to 36 healthy controls, Alboniga and co-workers reported significantly elevated ADMA and SDMA concentrations as well as decreased L-citrulline concentrations in COVID-19 patients 16 .Hasimi and colleagues compared 57 patients with moderately severe COVID-19 disease with 29 patients with severe COVID-19 and 21 healthy controls.They found significantly elevated ADMA in all COVID-19 patients as compared to controls, and a gradual increase in SDMA which was highest in patients with severe COVID-19 17 .In another study using a sandwich ELISA technique to measure ADMA, Karacaer and colleagues reported a higher ADMA increase in patients with severe COVID-19 than in those with a mild disease course 18 .
L-arginine metabolism is involved in numerous biochemical processes that play a role during systemic inflammation: L-arginine serves as a substrate for NO synthase (NOS), where the inducible isoform of NOS (iNOS) is involved in unspecific host defense 7 .At the same time, L-arginine is also the substrate for endothelial NOS, which generates NO by a much smaller catalytic rate and is an important physiological regulator of the homeostasis in vascular tone and vascular function 28 .From our measurements of L-arginine and L-citrulline, the by-product of NOS, we cannot deduct which isoform of NOS was responsible for the conversion of L-arginine to L-citrulline; therefore, this metabolic ratio remains a rough surrogate measure of total NOS activity-even more so as other biochemical pathways may also affect L-arginine and L-citrulline concentrations.However, the differences in metabolite concentrations between survivors and non-survivors that we noted in our study are suggestive of higher NOS activity and higher arginase activity in patients who died of COVID-19.Upregulation of inducible iNOS is commonly seen in sepsis patients, where excessive NO production contributes to low blood pressure which may lead to septic shock 29 .Interestingly, arterial blood pressure was lower in patients who died than in survivors within our multicenter cohort, supporting the hypothesis that iNOS induction may have contributed to their fatal outcome.
L-arginine can also be converted to L-ornithine by either of two isoforms of arginase 30 .The Orn/Arg Ratio serves as a metabolic surrogate marker of arginase activity; however, the metabolic flux of L-ornithine to, e.g., polyamines or collagen, also determines its levels.Whilst arginase-1 is highly expressed in hepatocytes, red blood cells, and immune cells, arginase-2 is a more ubiquitous enzyme that can be upregulated by inflammatory cytokines 30 .Therefore, we speculate that the systemic inflammation present in those COVID-19 patients that ultimately did not survive hospitalization may have contributed to higher arginase activity and, thus, higher Orn/Arg Ratio in our study.
The core result of our study is that ADMA and SDMA are elevated in hospitalized COVID-19 patients at the time of hospitalization.Although our study is limited by the fact that we were unable to include healthy controls of high age, the finding that the levels of ADMA and SDMA were even higher in patients with high risk of mortality and further increased in non-survivors as compared to survivors strongly suggest that the differences we measured between groups are meaningful.In the quest for more reliable risk predictors of COVID-19-associated mortality, our previous pilot study as well as a small number of other studies with small patient numbers suggested that ADMA and SDMA may be suitable for risk prediction in COVID-19.We confirm these prior observations here in a multicenter study design reporting that cut-off values of 0.599 µmol/L for ADMA and 0.579 µmol/L for SDMA are optimal discriminators of high-and low-risk COVID-19 patients during their hospitalization.
Our study is limited by the fact that the control group was matched by age and sex, but not for underlying health conditions.Thus, some of the difference in ADMA and SDMA concentrations between COVID-19 patients and healthy controls may have been due to the difference in mean age and the absence of chronic diseases like chronic lung disease, heart diseases, and diabetes mellitus in the control group.To this end, we performed a supplementary analysis restricted to 127 fully age-matched patients and controls, in which we found very closely similar differences in mean biomarker concentrations like in the complete dataset.Therefore, the known age-dependence of plasma ADMA and SDMA concentrations appears not to have had a major impact on the differences in biomarker levels in the present study.In addition, the major comparison in our study was between survivors and non-survivors among COVID-19 patients.There were some differences in the prevalence of comorbidities between survivors and non-survivors as displayed in Table 1.However, the "net effect" of the comorbidities on ADMA and SDMA levels is difficult to account for, as, for example, chronic lung disease, which is associated with high ADMA, was more prevalent in non-survivors, whilst cardiovascular disease and chronic kidney disease, which are also associated with high ADMA, were less prevalent in non-survivors.
The mechanism leading to upregulation of SDMA and ADMA concentrations may be related to systemic inflammation.We recently reported associations of these two biomarkers with inflammation in the populationbased SHIP cohort 31 .In previous studies, we had found a strong interrelation between ADMA and CRP in predicting the progression of vascular intimal damage in hemodialysis patients 32 , and proposed that upregulation of DDAH activity during sepsis might counterbalance induction of iNOS activity 33 .Accordingly, in sepsis patients, we found that SDMA and ADMA predicted survival in a cohort of 120 ICU-treated patients with sepsis 9 .In the latter study, SDMA was correlated with pro-calcitonin and creatinine serum levels whilst ADMA was not.
Both markers were correlated with lactate levels, which is suggestive of a relation to microcirculatory failure and tissue hypoxia.Finally, our previous study 19 demonstrated that amongst COVID-19 patients classified into low, intermediate, and high risk groups by a machine-learning approach, ADMA was highest in the high-risk group and it was associated by absence of pulmonary vasodilation as analyzed in thoracic CT scan.These findings are in line with previous work by our group dissecting the molecular mechanisms of regulation of ADMA and SDMA in pulmonary hypoxia and the role of ADMA in modulating hypoxic pulmonary vasoconstriction 34 .Clearly, a mechanistic link of SARS-CoV-2 virus to ADMA and SDMA metabolism cannot be established from our clinical cohort study, but the existing data from the present study and the previous studies cited above clearly warrant further research into possible mechanisms.Systemic inflammation and pulmonary hypoxia might be two conditions worth being explored in this context.
In conclusion, our study strongly suggests that adding ADMA and SDMA as biomarkers to our portfolio of diagnostic assessment in hospitalized COVID-19 patients provides significant benefit for early discrimination of patients with a severe, life-threatening disease progression.This may help to better allocate resources to patients at high risk of in-hospital mortality.

Figure 1 .
Figure 1.CONSORT flow diagram of our study.

Figure 2 .
Figure 2. Box plots showing the serum concentrations of ADMA (a), SDMA (b), the L-ornithine/L-arginine ratio (c), and the L-citrulline/L-arginine ratio (d) in hospitalized Covid-19 patients who survived or died during hospitalization, as compared to age-and sex-matched healthy controls.Boxes show the median and interquartile range of the data, with whiskers representing the 2.5th to 97.5th percentiles; data points outside of this distribution are plotted individually.Statistical significances were calculated by one-way ANOVA followed by Tukey's multiple comparisons test.ADMA, asymmetric dimethylarginine; Cit/Arg Ratio, L-citrulline/L-arginine ratio; Orn/Arg Ratio, L-ornithine/L-arginine ratio; SDMA, symmetric dimethylarginine.

Figure 3 .
Figure 3. Receiver-operated curve (ROC) charts for SDMA (a) and ADMA (b), and Kaplan-Meier survival curves for SDMA (c) and ADMA (d).The serum concentration allowing optimal discrimination between Covid-19 survivors and non-survivors is marked by arrows in (a) and (b); survival curves were constructed by splitting data at these pre-determined concentrations.ADMA, asymmetric dimethylarginine; AUC, area under the curve; OR, odds ratio; SDMA, symmetric dimethylarginine.

Figure 4 .
Figure 4. Kaplan-Meier survival curves for combined analysis of ADMA and SDMA serum concentrations.Both low means both biomarker concentrations below their respective cut-off levels from ROC analysis (see Fig. 2a,b); intermediate means that one biomarker is above and the other is below the respective cut-off level; both high indicates that both biomarkers were above their respective cut-off levels.OR, odds ratio.

Figure 6 .
Figure 6.Time course of serum concentrations of SDMA and ADMA in Covid-19 patients who survived (a,c) or died (b,d) during hospitalization.The coloured lines indicate the groups' means and standard deviations at each time point (blue, survivors, red, non-survivors).Dotted horizontal lines in plots (b) and (d) mark the time that elapsed until the day of death of COVID-19 non-survivors.ADMA, asymmetric dimethylarginine; SDMA, symmetric dimethylarginine.

Table 1 .
Baseline characteristics of the study cohort by survival.Data are mean ± standard deviation if not indicated otherwise.ARDS, acute respiratory distress syndrome; BMI, body mass index; ECMO, extracorporeal membrane oxygenation; ICU, intensive care unit; SOFA score, sepsis-related organ failure assessment score; SaO 2 , arterial oxygen saturation.

Table 2 .
Biomarker concentrations at baseline by survival.Significant values are in bold.Data are mean ± standard deviation.Concentrations are given as µmol/L.ADMA, asymmetric dimethylarginine; n.a., not assessed; n.s., not significant; SDMA, symmetric dimethylarginine.P values are from oneway ANOVA with adjustment of p for multiple testing.

Table 5 .
Predictive power of the SOFA score, C-reactive protein levels, and pro-calcitonin levels at admission for in-hospital mortality, when analyzed alone or in combination with SDMA or ADMA.
a Mortality of the subgroups is given for the dichotomized SOFA score, C-reactive protein, and pro-calcitonin values when analyzed alone (no intermediate risk group), as well as for the highest risk group (traditional risk marker high plus SDMA/ADMA high), the intermediate risk group (traditional risk marker or SDMA/ADMA high), and lowest risk group (traditional risk marker plus SDMA/ADMA low).*Pdenotes statistical significance level for trend across all risk groups.#p denotes statistical significance level for the hazard ratio of the high versus lowrisk groups.ADMA, asymmetric dimethylarginine; SDMA, symmetric dimethylarginine; SOFA, sequential organ failure assessment.Parameter Mortality [